{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1dd446cc",
   "metadata": {},
   "source": [
    "# 1. 模型配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62a8d3c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luyingli\\.conda\\envs\\yolo5\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LlamaConfig {\n",
       "  \"attention_bias\": false,\n",
       "  \"attention_dropout\": 0.0,\n",
       "  \"bos_token_id\": 1,\n",
       "  \"eos_token_id\": 2,\n",
       "  \"hidden_act\": \"silu\",\n",
       "  \"hidden_size\": 256,\n",
       "  \"initializer_range\": 0.02,\n",
       "  \"intermediate_size\": 768,\n",
       "  \"max_position_embeddings\": 2048,\n",
       "  \"model_type\": \"llama\",\n",
       "  \"num_attention_heads\": 16,\n",
       "  \"num_hidden_layers\": 4,\n",
       "  \"num_key_value_heads\": 8,\n",
       "  \"pretraining_tp\": 1,\n",
       "  \"rms_norm_eps\": 1e-06,\n",
       "  \"rope_scaling\": null,\n",
       "  \"rope_theta\": 10000.0,\n",
       "  \"tie_word_embeddings\": false,\n",
       "  \"transformers_version\": \"4.39.3\",\n",
       "  \"use_cache\": true,\n",
       "  \"vocab_size\": 32000\n",
       "}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoConfig\n",
    "\n",
    "\n",
    "config = AutoConfig.for_model(\n",
    "    model_type='llama',\n",
    "    hidden_size=256,\n",
    "    intermediate_size=768,\n",
    "    num_attention_heads=16,\n",
    "    num_hidden_layers=4,\n",
    "    num_key_value_heads=8\n",
    ")\n",
    "config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8dfd6df",
   "metadata": {},
   "source": [
    "# 2. 构建llama模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0593c917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LlamaForCausalLM(\n",
       "  (model): LlamaModel(\n",
       "    (embed_tokens): Embedding(32000, 256)\n",
       "    (layers): ModuleList(\n",
       "      (0-3): 4 x LlamaDecoderLayer(\n",
       "        (self_attn): LlamaAttention(\n",
       "          (q_proj): Linear(in_features=256, out_features=256, bias=False)\n",
       "          (k_proj): Linear(in_features=256, out_features=128, bias=False)\n",
       "          (v_proj): Linear(in_features=256, out_features=128, bias=False)\n",
       "          (o_proj): Linear(in_features=256, out_features=256, bias=False)\n",
       "          (rotary_emb): LlamaRotaryEmbedding()\n",
       "        )\n",
       "        (mlp): LlamaMLP(\n",
       "          (gate_proj): Linear(in_features=256, out_features=768, bias=False)\n",
       "          (up_proj): Linear(in_features=256, out_features=768, bias=False)\n",
       "          (down_proj): Linear(in_features=768, out_features=256, bias=False)\n",
       "          (act_fn): SiLU()\n",
       "        )\n",
       "        (input_layernorm): LlamaRMSNorm()\n",
       "        (post_attention_layernorm): LlamaRMSNorm()\n",
       "      )\n",
       "    )\n",
       "    (norm): LlamaRMSNorm()\n",
       "  )\n",
       "  (lm_head): Linear(in_features=256, out_features=32000, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model = AutoModelForCausalLM.from_config(config,torch_dtype=torch.float32).to(device)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76f6fca",
   "metadata": {},
   "source": [
    "# 3. 构建中文分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "095ce988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertTokenizerFast(name_or_path='./bert-base-chinese', vocab_size=21128, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
       "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "# tokenizer = AutoTokenizer.from_pretrained('./chinese_llama')\n",
    "tokenizer = AutoTokenizer.from_pretrained(r\"./bert-base-chinese\", trust_remote_code=True)\n",
    "\n",
    "tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2614902",
   "metadata": {},
   "source": [
    "# 4. 打印模型的每一层及其参数大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71551ea2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer Name & Parameters\n",
      "----------------------------\n",
      "model.embed_tokens.weight                          | Size: torch.Size([32000, 256])       | Count: 8192000             \n",
      "model.layers.0.self_attn.q_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.0.self_attn.k_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.0.self_attn.v_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.0.self_attn.o_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.0.mlp.gate_proj.weight                | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.0.mlp.up_proj.weight                  | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.0.mlp.down_proj.weight                | Size: torch.Size([256, 768])         | Count: 196608              \n",
      "model.layers.0.input_layernorm.weight              | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.0.post_attention_layernorm.weight     | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.1.self_attn.q_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.1.self_attn.k_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.1.self_attn.v_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.1.self_attn.o_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.1.mlp.gate_proj.weight                | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.1.mlp.up_proj.weight                  | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.1.mlp.down_proj.weight                | Size: torch.Size([256, 768])         | Count: 196608              \n",
      "model.layers.1.input_layernorm.weight              | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.1.post_attention_layernorm.weight     | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.2.self_attn.q_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.2.self_attn.k_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.2.self_attn.v_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.2.self_attn.o_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.2.mlp.gate_proj.weight                | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.2.mlp.up_proj.weight                  | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.2.mlp.down_proj.weight                | Size: torch.Size([256, 768])         | Count: 196608              \n",
      "model.layers.2.input_layernorm.weight              | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.2.post_attention_layernorm.weight     | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.3.self_attn.q_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.3.self_attn.k_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.3.self_attn.v_proj.weight             | Size: torch.Size([128, 256])         | Count: 32768               \n",
      "model.layers.3.self_attn.o_proj.weight             | Size: torch.Size([256, 256])         | Count: 65536               \n",
      "model.layers.3.mlp.gate_proj.weight                | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.3.mlp.up_proj.weight                  | Size: torch.Size([768, 256])         | Count: 196608              \n",
      "model.layers.3.mlp.down_proj.weight                | Size: torch.Size([256, 768])         | Count: 196608              \n",
      "model.layers.3.input_layernorm.weight              | Size: torch.Size([256])              | Count: 256                 \n",
      "model.layers.3.post_attention_layernorm.weight     | Size: torch.Size([256])              | Count: 256                 \n",
      "model.norm.weight                                  | Size: torch.Size([256])              | Count: 256                 \n",
      "lm_head.weight                                     | Size: torch.Size([32000, 256])       | Count: 8192000             \n",
      "----------------------------\n",
      "Total Parameters: 19532032 (19.5 M)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def print_model_parameters(model):\n",
    "    print('Layer Name & Parameters')    \n",
    "    print('----------------------------')    \n",
    "    total_params = 0    \n",
    "    for name, parameter in model.named_parameters():    \n",
    "        param_size = parameter.size()        \n",
    "        param_count = torch.prod(torch.tensor(param_size)).item()        \n",
    "        total_params += param_count        \n",
    "        print(f'{name:50} | Size: {str(param_size):30} | Count: {str(param_count):20}')    \n",
    "    print('----------------------------')    \n",
    "    print(f'Total Parameters: {total_params} ({total_params / 1000000:.1f} M)')\n",
    "\n",
    "print_model_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a9c5c6",
   "metadata": {},
   "source": [
    "# 5. 预推理，检测模型是否能跑通"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c635e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "那 咋 啦帆繕墓 证恢ь 蓑 蜆傅 蓑 刨 牠漢 刨 貅 6606薪\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# 设置环境变量\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
    "\n",
    "\n",
    "def inference(\n",
    "    model: AutoModelForCausalLM,    \n",
    "    tokenizer: AutoTokenizer,    \n",
    "    input_text: str = '那咋啦 ',    \n",
    "    max_new_tokens: int = 16\n",
    "):\n",
    "    inputs = tokenizer(input_text, return_tensors='pt').to(device)  \n",
    "    if 'token_type_ids' in inputs:\n",
    "        del inputs['token_type_ids']\n",
    "    outputs = model.generate(    \n",
    "        **inputs,        \n",
    "        pad_token_id=tokenizer.pad_token_id,        \n",
    "        max_new_tokens=max_new_tokens,        \n",
    "        do_sample=True,        \n",
    "        top_k=40,       \n",
    "        top_p=0.95,        \n",
    "        temperature=0.8    \n",
    "    )   \n",
    "    generated_text = tokenizer.decode( \n",
    "        outputs[0],        \n",
    "        skip_special_tokens=True    \n",
    "    )    \n",
    "    # print(outputs)    \n",
    "    print(generated_text)\n",
    "inference(model, tokenizer, input_text = '那咋啦 ', max_new_tokens=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a836981",
   "metadata": {},
   "source": [
    "# 6. Kaiming 初始化模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26b817ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kaiming_initialization(model):\n",
    "    for name, param in model.named_parameters():   \n",
    "        if 'weight' in name and param.dim() > 1:            \n",
    "            torch.nn.init.kaiming_uniform_(param, mode='fan_in', nonlinearity='leaky_relu')        \n",
    "        elif 'bias' in name:            \n",
    "            # 一般偏置项可以初始化为 0            \n",
    "            torch.nn.init.constant_(param, 0)\n",
    "        \n",
    "kaiming_initialization(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ed93be",
   "metadata": {},
   "source": [
    "# 7.加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2046112",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset_name_or_path = './AdvertiseGen' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52962fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_train = load_dataset(dataset_name_or_path, split='train')    # 只取前 10 %，约 270k 条\n",
    "ds_val = load_dataset(dataset_name_or_path, split='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b321b75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'content': ['类型#裤*版型#宽松*风格#性感*图案#线条*裤型#阔腿裤', '类型#裙*风格#简约*图案#条纹*图案#线条*图案#撞色*裙型#鱼尾裙*裙袖长#无袖', '类型#上衣*版型#宽松*颜色#粉红色*图案#字母*图案#文字*图案#线条*衣样式#卫衣*衣款式#不规则', '类型#裙*版型#宽松*材质#雪纺*风格#清新*裙型#a字*裙长#连衣裙'], 'summary': ['宽松的阔腿裤这两年真的吸粉不少，明星时尚达人的心头爱。毕竟好穿时尚，谁都能穿出腿长2米的效果宽松的裤腿，当然是遮肉小能手啊。上身随性自然不拘束，面料亲肤舒适贴身体验感棒棒哒。系带部分增加设计看点，还让单品的设计感更强。腿部线条若隐若现的，性感撩人。颜色敲温柔的，与裤子本身所呈现的风格有点反差萌。', '圆形领口修饰脖颈线条，适合各种脸型，耐看有气质。无袖设计，尤显清凉，简约横条纹装饰，使得整身人鱼造型更为生动立体。加之撞色的鱼尾下摆，深邃富有诗意。收腰包臀,修饰女性身体曲线，结合别出心裁的鱼尾裙摆设计，勾勒出自然流畅的身体轮廓，展现了婀娜多姿的迷人姿态。', '宽松的卫衣版型包裹着整个身材，宽大的衣身与身材形成鲜明的对比描绘出纤瘦的身形。下摆与袖口的不规则剪裁设计，彰显出时尚前卫的形态。被剪裁过的样式呈现出布条状自然地垂坠下来，别具有一番设计感。线条分明的字母样式有着花式的外观，棱角分明加上具有少女元气的枣红色十分有年轻活力感。粉红色的衣身把肌肤衬托得很白嫩又健康。', '踩着轻盈的步伐享受在午后的和煦风中，让放松与惬意感为你免去一身的压力与束缚，仿佛要将灵魂也寄托在随风摇曳的雪纺连衣裙上，吐露出<UNK>微妙而又浪漫的清新之意。宽松的a字版型除了能够带来足够的空间，也能以上窄下宽的方式强化立体层次，携带出自然优雅的曼妙体验。']}\n"
     ]
    }
   ],
   "source": [
    "# 查看前两条\n",
    "print(ds_train[:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d38456a",
   "metadata": {},
   "source": [
    "# 8. 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1282c211",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_function(examples):\n",
    "    inputs = examples[\"content\"]\n",
    "    targets = examples[\"summary\"]\n",
    "    \n",
    "    # 编码输入\n",
    "    model_inputs = tokenizer(inputs, max_length=256, truncation=True, padding='max_length')\n",
    "\n",
    "    # 编码标签，并添加 labels\n",
    "    with tokenizer.as_target_tokenizer():\n",
    "        labels = tokenizer(targets, max_length=256, truncation=True, padding='max_length')\n",
    "\n",
    "    # 将标签的特殊token填充部分替换为-100，避免影响损失计算\n",
    "    labels[\"input_ids\"] = [\n",
    "        [(label if label != tokenizer.pad_token_id else -100) for label in labels_list] \n",
    "        for labels_list in labels[\"input_ids\"]\n",
    "    ]\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    \n",
    "    return model_inputs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ab3ab2",
   "metadata": {},
   "source": [
    "# 9. 数据加载器和数据集处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "069a0c7b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DataLoader' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m tokenized_train_dataset \u001b[38;5;241m=\u001b[39m ds_train\u001b[38;5;241m.\u001b[39mmap(preprocess_function, batched\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      6\u001b[0m tokenized_val_dataset \u001b[38;5;241m=\u001b[39m ds_val\u001b[38;5;241m.\u001b[39mmap(preprocess_function, batched\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m----> 7\u001b[0m val_dataloader \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoader\u001b[49m(tokenized_val_dataset, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, collate_fn\u001b[38;5;241m=\u001b[39mdata_collator, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'DataLoader' is not defined"
     ]
    }
   ],
   "source": [
    "from transformers import DataCollatorForSeq2Seq\n",
    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n",
    "\n",
    "tokenized_train_dataset = ds_train.map(preprocess_function, batched=True)\n",
    "\n",
    "tokenized_val_dataset = ds_val.map(preprocess_function, batched=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17306901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'content': '类型#上衣*材质#牛仔布*颜色#白色*风格#简约*图案#刺绣*衣样式#外套*衣款式#破洞', 'summary': '简约而不简单的牛仔外套，白色的衣身十分百搭。衣身多处有做旧破洞设计，打破单调乏味，增加一丝造型看点。衣身后背处有趣味刺绣装饰，丰富层次感，彰显别样时尚。', 'input_ids': [101, 5102, 1798, 108, 677, 6132, 115, 3332, 6574, 108, 4281, 798, 2357, 115, 7582, 5682, 108, 4635, 5682, 115, 7599, 3419, 108, 5042, 5276, 115, 1745, 3428, 108, 1173, 5323, 115, 6132, 3416, 2466, 108, 1912, 1947, 115, 6132, 3621, 2466, 108, 4788, 3822, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [101, 5042, 5276, 5445, 679, 5042, 1296, 4638, 4281, 798, 1912, 1947, 8024, 4635, 5682, 4638, 6132, 6716, 1282, 1146, 4636, 3022, 511, 6132, 6716, 1914, 1905, 3300, 976, 3191, 4788, 3822, 6392, 6369, 8024, 2802, 4788, 1296, 6444, 726, 1456, 8024, 1872, 1217, 671, 692, 6863, 1798, 4692, 4157, 511, 6132, 6716, 1400, 5520, 1905, 3300, 6637, 1456, 1173, 5323, 6163, 7652, 8024, 705, 2168, 2231, 3613, 2697, 8024, 2511, 3227, 1166, 3416, 3198, 2213, 511, 102, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]}\n"
     ]
    }
   ],
   "source": [
    "for i in tokenized_val_dataset:\n",
    "    print(i)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5c66e6",
   "metadata": {},
   "source": [
    "# 10. 设置训练参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f160799",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='saves',                         # 输出路径，包括模型检查点、中间文件等    \n",
    "    overwrite_output_dir=True,                  # 是否覆写 \n",
    "    do_train=True,                              # 是否做训练    \n",
    "    do_eval=True,                               # 是否做评估    \n",
    "    eval_steps=1000,                            # 评估步骤间隔    \n",
    "    per_device_train_batch_size=4,              # 每设备批次    \n",
    "    gradient_accumulation_steps=1,              # 梯度累计步大小，省显存，但小模型没必要，用 1 收敛比较快    \n",
    "    learning_rate=1e-4,                         # 学习率大小    \n",
    "    lr_scheduler_type='cosine',                 # 学习率调度策略，LLM 训练一般都用余弦    \n",
    "    bf16=torch.cuda.is_bf16_supported(),        # 尝试配置 bf16    \n",
    "    fp16=not torch.cuda.is_bf16_supported(),    # bf16 不行就上 fp16    \n",
    "    logging_steps=50,                           # 打印步骤间隔    report_to=None,                             # 日志输出目标，不想用 wandb 可以设置为 None    \n",
    "    num_train_epochs=2,                         # 训练轮数，2 ~ 3 即可    \n",
    "    save_steps=1000,                            # 检查点保存步骤间隔    \n",
    "    save_total_limit=2,                         # output_dir 内留存的检查点最大数目    seed=3407                                   # 随机种子\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf0bab06",
   "metadata": {},
   "source": [
    "# 11. 定义Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2af1d806",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_train_dataset,\n",
    "    eval_dataset=tokenized_val_dataset,\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836c8132",
   "metadata": {},
   "source": [
    "# 12. 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2214a2dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='57300' max='57300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [57300/57300 1:31:44, Epoch 2/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>9.597400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>7.410600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>6.448400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>6.092500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>6.006900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>5.969400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>5.875500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>5.877800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>5.865800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>5.812600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>5.856700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>5.845500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>5.837400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>5.831900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>5.825500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>5.850200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>850</td>\n",
       "      <td>5.838900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>5.813400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>950</td>\n",
       "      <td>5.819200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>5.812200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1050</td>\n",
       "      <td>5.829900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>5.809300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1150</td>\n",
       "      <td>5.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>5.773000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1250</td>\n",
       "      <td>5.760900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>5.785400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1350</td>\n",
       "      <td>5.784800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1400</td>\n",
       "      <td>5.777000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1450</td>\n",
       "      <td>5.758600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>5.767000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1550</td>\n",
       "      <td>5.802800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1600</td>\n",
       "      <td>5.816200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1650</td>\n",
       "      <td>5.770900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1700</td>\n",
       "      <td>5.777700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1750</td>\n",
       "      <td>5.797600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1800</td>\n",
       "      <td>5.802200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1850</td>\n",
       "      <td>5.824600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1900</td>\n",
       "      <td>5.762400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1950</td>\n",
       "      <td>5.762800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>5.746400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2050</td>\n",
       "      <td>5.800200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2100</td>\n",
       "      <td>5.761600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2150</td>\n",
       "      <td>5.776200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2200</td>\n",
       "      <td>5.779400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2250</td>\n",
       "      <td>5.742800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2300</td>\n",
       "      <td>5.759900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2350</td>\n",
       "      <td>5.720800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2400</td>\n",
       "      <td>5.772200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2450</td>\n",
       "      <td>5.757300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>5.748900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2550</td>\n",
       "      <td>5.735500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2600</td>\n",
       "      <td>5.788500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2650</td>\n",
       "      <td>5.789400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2700</td>\n",
       "      <td>5.778900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2750</td>\n",
       "      <td>5.736700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2800</td>\n",
       "      <td>5.704900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2850</td>\n",
       "      <td>5.762200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2900</td>\n",
       "      <td>5.724200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2950</td>\n",
       "      <td>5.739700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>5.740400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3050</td>\n",
       "      <td>5.732700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3100</td>\n",
       "      <td>5.789300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3150</td>\n",
       "      <td>5.755600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3200</td>\n",
       "      <td>5.756000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3250</td>\n",
       "      <td>5.739500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3300</td>\n",
       "      <td>5.749200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3350</td>\n",
       "      <td>5.733000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3400</td>\n",
       "      <td>5.722200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3450</td>\n",
       "      <td>5.735400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>5.711900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3550</td>\n",
       "      <td>5.684900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3600</td>\n",
       "      <td>5.682300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3650</td>\n",
       "      <td>5.720200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3700</td>\n",
       "      <td>5.733200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3750</td>\n",
       "      <td>5.731100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3800</td>\n",
       "      <td>5.713400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3850</td>\n",
       "      <td>5.717500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3900</td>\n",
       "      <td>5.752000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3950</td>\n",
       "      <td>5.740500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>5.738300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4050</td>\n",
       "      <td>5.746500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4100</td>\n",
       "      <td>5.710500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4150</td>\n",
       "      <td>5.747200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4200</td>\n",
       "      <td>5.728000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4250</td>\n",
       "      <td>5.725200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4300</td>\n",
       "      <td>5.712600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4350</td>\n",
       "      <td>5.696500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4400</td>\n",
       "      <td>5.684000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4450</td>\n",
       "      <td>5.724300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>5.704800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4550</td>\n",
       "      <td>5.673900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4600</td>\n",
       "      <td>5.706500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4650</td>\n",
       "      <td>5.703300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4700</td>\n",
       "      <td>5.751800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4750</td>\n",
       "      <td>5.708500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4800</td>\n",
       "      <td>5.710200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4850</td>\n",
       "      <td>5.715500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4900</td>\n",
       "      <td>5.686300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4950</td>\n",
       "      <td>5.696400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>5.704300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5050</td>\n",
       "      <td>5.717600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5100</td>\n",
       "      <td>5.743500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5150</td>\n",
       "      <td>5.695100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5200</td>\n",
       "      <td>5.696800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5250</td>\n",
       "      <td>5.734100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5300</td>\n",
       "      <td>5.669700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5350</td>\n",
       "      <td>5.688800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5400</td>\n",
       "      <td>5.700600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5450</td>\n",
       "      <td>5.698900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>5.688800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5550</td>\n",
       "      <td>5.689800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5600</td>\n",
       "      <td>5.681100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5650</td>\n",
       "      <td>5.708800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5700</td>\n",
       "      <td>5.686300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5750</td>\n",
       "      <td>5.716600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5800</td>\n",
       "      <td>5.701000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5850</td>\n",
       "      <td>5.673300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5900</td>\n",
       "      <td>5.687900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5950</td>\n",
       "      <td>5.735000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>5.727500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6050</td>\n",
       "      <td>5.672900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6100</td>\n",
       "      <td>5.681200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6150</td>\n",
       "      <td>5.699500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6200</td>\n",
       "      <td>5.692300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6250</td>\n",
       "      <td>5.674900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6300</td>\n",
       "      <td>5.715900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6350</td>\n",
       "      <td>5.716900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6400</td>\n",
       "      <td>5.703700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6450</td>\n",
       "      <td>5.668500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6500</td>\n",
       "      <td>5.684800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6550</td>\n",
       "      <td>5.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6600</td>\n",
       "      <td>5.702100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6650</td>\n",
       "      <td>5.649100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6700</td>\n",
       "      <td>5.665400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6750</td>\n",
       "      <td>5.695800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6800</td>\n",
       "      <td>5.685400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6850</td>\n",
       "      <td>5.673000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6900</td>\n",
       "      <td>5.691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6950</td>\n",
       "      <td>5.668400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>5.659300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7050</td>\n",
       "      <td>5.666800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7100</td>\n",
       "      <td>5.666200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7150</td>\n",
       "      <td>5.664700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7200</td>\n",
       "      <td>5.695400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7250</td>\n",
       "      <td>5.655600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7300</td>\n",
       "      <td>5.697900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7350</td>\n",
       "      <td>5.704300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7400</td>\n",
       "      <td>5.654700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7450</td>\n",
       "      <td>5.714100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>5.638800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7550</td>\n",
       "      <td>5.675500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7600</td>\n",
       "      <td>5.712300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7650</td>\n",
       "      <td>5.688400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7700</td>\n",
       "      <td>5.658100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7750</td>\n",
       "      <td>5.708200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7800</td>\n",
       "      <td>5.668500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7850</td>\n",
       "      <td>5.706800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7900</td>\n",
       "      <td>5.648500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7950</td>\n",
       "      <td>5.699000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>5.661600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8050</td>\n",
       "      <td>5.651100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8100</td>\n",
       "      <td>5.702900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8150</td>\n",
       "      <td>5.694700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8200</td>\n",
       "      <td>5.690100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8250</td>\n",
       "      <td>5.694500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8300</td>\n",
       "      <td>5.679800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8350</td>\n",
       "      <td>5.660500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8400</td>\n",
       "      <td>5.654100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8450</td>\n",
       "      <td>5.665300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8500</td>\n",
       "      <td>5.703200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8550</td>\n",
       "      <td>5.674400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8600</td>\n",
       "      <td>5.642500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8650</td>\n",
       "      <td>5.679700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8700</td>\n",
       "      <td>5.686000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8750</td>\n",
       "      <td>5.684000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8800</td>\n",
       "      <td>5.654300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8850</td>\n",
       "      <td>5.687400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8900</td>\n",
       "      <td>5.664600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8950</td>\n",
       "      <td>5.699100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>5.703300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9050</td>\n",
       "      <td>5.657300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9100</td>\n",
       "      <td>5.667100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9150</td>\n",
       "      <td>5.641300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9200</td>\n",
       "      <td>5.632600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9250</td>\n",
       "      <td>5.669200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9300</td>\n",
       "      <td>5.683400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9350</td>\n",
       "      <td>5.694900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9400</td>\n",
       "      <td>5.658900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9450</td>\n",
       "      <td>5.627100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9500</td>\n",
       "      <td>5.652400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9550</td>\n",
       "      <td>5.677600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9600</td>\n",
       "      <td>5.667100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9650</td>\n",
       "      <td>5.684200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9700</td>\n",
       "      <td>5.678600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9750</td>\n",
       "      <td>5.688400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9800</td>\n",
       "      <td>5.668300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9850</td>\n",
       "      <td>5.626700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9900</td>\n",
       "      <td>5.668600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9950</td>\n",
       "      <td>5.675200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>5.683900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10050</td>\n",
       "      <td>5.686700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10100</td>\n",
       "      <td>5.654400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10150</td>\n",
       "      <td>5.654700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10200</td>\n",
       "      <td>5.634600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10250</td>\n",
       "      <td>5.649600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10300</td>\n",
       "      <td>5.686300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10350</td>\n",
       "      <td>5.669700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10400</td>\n",
       "      <td>5.615400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10450</td>\n",
       "      <td>5.646100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10500</td>\n",
       "      <td>5.667400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10550</td>\n",
       "      <td>5.641700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10600</td>\n",
       "      <td>5.679900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10650</td>\n",
       "      <td>5.649100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10700</td>\n",
       "      <td>5.625800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10750</td>\n",
       "      <td>5.648300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10800</td>\n",
       "      <td>5.667200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10850</td>\n",
       "      <td>5.656200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10900</td>\n",
       "      <td>5.647600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10950</td>\n",
       "      <td>5.679300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11000</td>\n",
       "      <td>5.628400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11050</td>\n",
       "      <td>5.660600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11100</td>\n",
       "      <td>5.645300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11150</td>\n",
       "      <td>5.664200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11200</td>\n",
       "      <td>5.637400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11250</td>\n",
       "      <td>5.662600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11300</td>\n",
       "      <td>5.655100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11350</td>\n",
       "      <td>5.675200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11400</td>\n",
       "      <td>5.662600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11450</td>\n",
       "      <td>5.635700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11500</td>\n",
       "      <td>5.643400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11550</td>\n",
       "      <td>5.694200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11600</td>\n",
       "      <td>5.698500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11650</td>\n",
       "      <td>5.652400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11700</td>\n",
       "      <td>5.631100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11750</td>\n",
       "      <td>5.621100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11800</td>\n",
       "      <td>5.664800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11850</td>\n",
       "      <td>5.632500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11900</td>\n",
       "      <td>5.640700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11950</td>\n",
       "      <td>5.661400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12000</td>\n",
       "      <td>5.674100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12050</td>\n",
       "      <td>5.649900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12100</td>\n",
       "      <td>5.635300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12150</td>\n",
       "      <td>5.632400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12200</td>\n",
       "      <td>5.638600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12250</td>\n",
       "      <td>5.674200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12300</td>\n",
       "      <td>5.671800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12350</td>\n",
       "      <td>5.653200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12400</td>\n",
       "      <td>5.618700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12450</td>\n",
       "      <td>5.649800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12500</td>\n",
       "      <td>5.678600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12550</td>\n",
       "      <td>5.663600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12600</td>\n",
       "      <td>5.627600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12650</td>\n",
       "      <td>5.651500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12700</td>\n",
       "      <td>5.679300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12750</td>\n",
       "      <td>5.619300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12800</td>\n",
       "      <td>5.646500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12850</td>\n",
       "      <td>5.621800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12900</td>\n",
       "      <td>5.627900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12950</td>\n",
       "      <td>5.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13000</td>\n",
       "      <td>5.626800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13050</td>\n",
       "      <td>5.675300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13100</td>\n",
       "      <td>5.640800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13150</td>\n",
       "      <td>5.621800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13200</td>\n",
       "      <td>5.619300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13250</td>\n",
       "      <td>5.637200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13300</td>\n",
       "      <td>5.621200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13350</td>\n",
       "      <td>5.651500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13400</td>\n",
       "      <td>5.658700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13450</td>\n",
       "      <td>5.646700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13500</td>\n",
       "      <td>5.668000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13550</td>\n",
       "      <td>5.663600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13600</td>\n",
       "      <td>5.635500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13650</td>\n",
       "      <td>5.593400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13700</td>\n",
       "      <td>5.660600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13750</td>\n",
       "      <td>5.656000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13800</td>\n",
       "      <td>5.632400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13850</td>\n",
       "      <td>5.626700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13900</td>\n",
       "      <td>5.656500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13950</td>\n",
       "      <td>5.642900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14000</td>\n",
       "      <td>5.673700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14050</td>\n",
       "      <td>5.648100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14100</td>\n",
       "      <td>5.613400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14150</td>\n",
       "      <td>5.680100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14200</td>\n",
       "      <td>5.634200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14250</td>\n",
       "      <td>5.594300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14300</td>\n",
       "      <td>5.631800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14350</td>\n",
       "      <td>5.633800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14400</td>\n",
       "      <td>5.642900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14450</td>\n",
       "      <td>5.659500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14500</td>\n",
       "      <td>5.610600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14550</td>\n",
       "      <td>5.631500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14600</td>\n",
       "      <td>5.640600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>5.584600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14700</td>\n",
       "      <td>5.648400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14750</td>\n",
       "      <td>5.639100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14800</td>\n",
       "      <td>5.628100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14850</td>\n",
       "      <td>5.651200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14900</td>\n",
       "      <td>5.642300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14950</td>\n",
       "      <td>5.628400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15000</td>\n",
       "      <td>5.641500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15050</td>\n",
       "      <td>5.654100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15100</td>\n",
       "      <td>5.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15150</td>\n",
       "      <td>5.578700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15200</td>\n",
       "      <td>5.631500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15250</td>\n",
       "      <td>5.637600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15300</td>\n",
       "      <td>5.646000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15350</td>\n",
       "      <td>5.631600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15400</td>\n",
       "      <td>5.624500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15450</td>\n",
       "      <td>5.684400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15500</td>\n",
       "      <td>5.615500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15550</td>\n",
       "      <td>5.643100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15600</td>\n",
       "      <td>5.632500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15650</td>\n",
       "      <td>5.633700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15700</td>\n",
       "      <td>5.607400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15750</td>\n",
       "      <td>5.636500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15800</td>\n",
       "      <td>5.600400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15850</td>\n",
       "      <td>5.629500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15900</td>\n",
       "      <td>5.657700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15950</td>\n",
       "      <td>5.616700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16000</td>\n",
       "      <td>5.631900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16050</td>\n",
       "      <td>5.619500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16100</td>\n",
       "      <td>5.636900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16150</td>\n",
       "      <td>5.611800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16200</td>\n",
       "      <td>5.620800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16250</td>\n",
       "      <td>5.624400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16300</td>\n",
       "      <td>5.654500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16350</td>\n",
       "      <td>5.652200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16400</td>\n",
       "      <td>5.618400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16450</td>\n",
       "      <td>5.616400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16500</td>\n",
       "      <td>5.617600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16550</td>\n",
       "      <td>5.610700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16600</td>\n",
       "      <td>5.655300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16650</td>\n",
       "      <td>5.631100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16700</td>\n",
       "      <td>5.660500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16750</td>\n",
       "      <td>5.658100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16800</td>\n",
       "      <td>5.661500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16850</td>\n",
       "      <td>5.646200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16900</td>\n",
       "      <td>5.633200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16950</td>\n",
       "      <td>5.635200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17000</td>\n",
       "      <td>5.632700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17050</td>\n",
       "      <td>5.651600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17100</td>\n",
       "      <td>5.644500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17150</td>\n",
       "      <td>5.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17200</td>\n",
       "      <td>5.602600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17250</td>\n",
       "      <td>5.592200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17300</td>\n",
       "      <td>5.617900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17350</td>\n",
       "      <td>5.668300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17400</td>\n",
       "      <td>5.650400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17450</td>\n",
       "      <td>5.579800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17500</td>\n",
       "      <td>5.646900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17550</td>\n",
       "      <td>5.617900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17600</td>\n",
       "      <td>5.631100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17650</td>\n",
       "      <td>5.595900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17700</td>\n",
       "      <td>5.659100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17750</td>\n",
       "      <td>5.613400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17800</td>\n",
       "      <td>5.645700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17850</td>\n",
       "      <td>5.627600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17900</td>\n",
       "      <td>5.648300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17950</td>\n",
       "      <td>5.631700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18000</td>\n",
       "      <td>5.604100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18050</td>\n",
       "      <td>5.656700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18100</td>\n",
       "      <td>5.649100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18150</td>\n",
       "      <td>5.661200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18200</td>\n",
       "      <td>5.617700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18250</td>\n",
       "      <td>5.606700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18300</td>\n",
       "      <td>5.656100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18350</td>\n",
       "      <td>5.613600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18400</td>\n",
       "      <td>5.627000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18450</td>\n",
       "      <td>5.663800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18500</td>\n",
       "      <td>5.584900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18550</td>\n",
       "      <td>5.613600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18600</td>\n",
       "      <td>5.635900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18650</td>\n",
       "      <td>5.629300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18700</td>\n",
       "      <td>5.615300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18750</td>\n",
       "      <td>5.651700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18800</td>\n",
       "      <td>5.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18850</td>\n",
       "      <td>5.646700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18900</td>\n",
       "      <td>5.633900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18950</td>\n",
       "      <td>5.603200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19000</td>\n",
       "      <td>5.612200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19050</td>\n",
       "      <td>5.630800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19100</td>\n",
       "      <td>5.652300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19150</td>\n",
       "      <td>5.626600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19200</td>\n",
       "      <td>5.622000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19250</td>\n",
       "      <td>5.635900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19300</td>\n",
       "      <td>5.641900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19350</td>\n",
       "      <td>5.627900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19400</td>\n",
       "      <td>5.639100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19450</td>\n",
       "      <td>5.634500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19500</td>\n",
       "      <td>5.611700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19550</td>\n",
       "      <td>5.641500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19600</td>\n",
       "      <td>5.604200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19650</td>\n",
       "      <td>5.620200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19700</td>\n",
       "      <td>5.620900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19750</td>\n",
       "      <td>5.603200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19800</td>\n",
       "      <td>5.649200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19850</td>\n",
       "      <td>5.634400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19900</td>\n",
       "      <td>5.651000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19950</td>\n",
       "      <td>5.631700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20000</td>\n",
       "      <td>5.635000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20050</td>\n",
       "      <td>5.612800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20100</td>\n",
       "      <td>5.634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20150</td>\n",
       "      <td>5.633400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20200</td>\n",
       "      <td>5.607000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20250</td>\n",
       "      <td>5.638300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20300</td>\n",
       "      <td>5.677700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20350</td>\n",
       "      <td>5.637700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20400</td>\n",
       "      <td>5.622100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20450</td>\n",
       "      <td>5.634200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20500</td>\n",
       "      <td>5.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20550</td>\n",
       "      <td>5.626400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20600</td>\n",
       "      <td>5.602700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20650</td>\n",
       "      <td>5.638200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20700</td>\n",
       "      <td>5.577100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20750</td>\n",
       "      <td>5.627700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20800</td>\n",
       "      <td>5.620700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20850</td>\n",
       "      <td>5.607600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20900</td>\n",
       "      <td>5.631300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20950</td>\n",
       "      <td>5.615700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21000</td>\n",
       "      <td>5.658100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21050</td>\n",
       "      <td>5.618500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21100</td>\n",
       "      <td>5.627000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21150</td>\n",
       "      <td>5.602600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21200</td>\n",
       "      <td>5.633400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21250</td>\n",
       "      <td>5.635800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21300</td>\n",
       "      <td>5.624300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21350</td>\n",
       "      <td>5.604100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21400</td>\n",
       "      <td>5.610300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21450</td>\n",
       "      <td>5.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21500</td>\n",
       "      <td>5.654700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21550</td>\n",
       "      <td>5.608800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21600</td>\n",
       "      <td>5.632200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21650</td>\n",
       "      <td>5.656200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21700</td>\n",
       "      <td>5.596800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21750</td>\n",
       "      <td>5.625200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21800</td>\n",
       "      <td>5.622300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21850</td>\n",
       "      <td>5.638200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21900</td>\n",
       "      <td>5.603000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21950</td>\n",
       "      <td>5.637000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22000</td>\n",
       "      <td>5.621400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22050</td>\n",
       "      <td>5.623800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22100</td>\n",
       "      <td>5.648800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22150</td>\n",
       "      <td>5.623300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22200</td>\n",
       "      <td>5.617000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22250</td>\n",
       "      <td>5.602200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22300</td>\n",
       "      <td>5.671600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22350</td>\n",
       "      <td>5.599000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22400</td>\n",
       "      <td>5.579300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22450</td>\n",
       "      <td>5.633900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22500</td>\n",
       "      <td>5.613000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22550</td>\n",
       "      <td>5.584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22600</td>\n",
       "      <td>5.595600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22650</td>\n",
       "      <td>5.628400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22700</td>\n",
       "      <td>5.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22750</td>\n",
       "      <td>5.622500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22800</td>\n",
       "      <td>5.600900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22850</td>\n",
       "      <td>5.598800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22900</td>\n",
       "      <td>5.646600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22950</td>\n",
       "      <td>5.587700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23000</td>\n",
       "      <td>5.620200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23050</td>\n",
       "      <td>5.625700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23100</td>\n",
       "      <td>5.651500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23150</td>\n",
       "      <td>5.619100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23200</td>\n",
       "      <td>5.627800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23250</td>\n",
       "      <td>5.647400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23300</td>\n",
       "      <td>5.602500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23350</td>\n",
       "      <td>5.606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23400</td>\n",
       "      <td>5.599100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23450</td>\n",
       "      <td>5.634900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23500</td>\n",
       "      <td>5.634800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23550</td>\n",
       "      <td>5.578000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23600</td>\n",
       "      <td>5.632800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23650</td>\n",
       "      <td>5.620300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23700</td>\n",
       "      <td>5.622600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23750</td>\n",
       "      <td>5.630500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23800</td>\n",
       "      <td>5.598100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23850</td>\n",
       "      <td>5.598100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23900</td>\n",
       "      <td>5.577600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23950</td>\n",
       "      <td>5.623200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24000</td>\n",
       "      <td>5.640500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24050</td>\n",
       "      <td>5.592300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24100</td>\n",
       "      <td>5.589000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24150</td>\n",
       "      <td>5.619900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24200</td>\n",
       "      <td>5.604600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24250</td>\n",
       "      <td>5.641000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24300</td>\n",
       "      <td>5.647000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24350</td>\n",
       "      <td>5.593200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24400</td>\n",
       "      <td>5.620200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24450</td>\n",
       "      <td>5.635100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24500</td>\n",
       "      <td>5.636800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24550</td>\n",
       "      <td>5.594600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24600</td>\n",
       "      <td>5.627900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24650</td>\n",
       "      <td>5.636300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24700</td>\n",
       "      <td>5.577600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24750</td>\n",
       "      <td>5.648500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24800</td>\n",
       "      <td>5.641000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24850</td>\n",
       "      <td>5.620200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24900</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24950</td>\n",
       "      <td>5.604900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25000</td>\n",
       "      <td>5.605200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25050</td>\n",
       "      <td>5.606200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25100</td>\n",
       "      <td>5.587200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25150</td>\n",
       "      <td>5.608700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25200</td>\n",
       "      <td>5.624700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25250</td>\n",
       "      <td>5.594000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25300</td>\n",
       "      <td>5.624900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25350</td>\n",
       "      <td>5.619400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25400</td>\n",
       "      <td>5.606800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25450</td>\n",
       "      <td>5.635800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25500</td>\n",
       "      <td>5.619500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25550</td>\n",
       "      <td>5.607100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25600</td>\n",
       "      <td>5.608000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25650</td>\n",
       "      <td>5.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25700</td>\n",
       "      <td>5.618600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25750</td>\n",
       "      <td>5.600900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25800</td>\n",
       "      <td>5.625100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25850</td>\n",
       "      <td>5.635400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25900</td>\n",
       "      <td>5.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25950</td>\n",
       "      <td>5.607000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26000</td>\n",
       "      <td>5.599300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26050</td>\n",
       "      <td>5.635500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26100</td>\n",
       "      <td>5.627500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26150</td>\n",
       "      <td>5.627000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26200</td>\n",
       "      <td>5.609700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26250</td>\n",
       "      <td>5.618000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26300</td>\n",
       "      <td>5.618100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26350</td>\n",
       "      <td>5.614800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26400</td>\n",
       "      <td>5.611400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26450</td>\n",
       "      <td>5.629800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26500</td>\n",
       "      <td>5.647500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26550</td>\n",
       "      <td>5.618300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26600</td>\n",
       "      <td>5.600200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26650</td>\n",
       "      <td>5.609100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26700</td>\n",
       "      <td>5.601600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26750</td>\n",
       "      <td>5.618400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26800</td>\n",
       "      <td>5.596600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26850</td>\n",
       "      <td>5.594100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26900</td>\n",
       "      <td>5.580900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26950</td>\n",
       "      <td>5.613700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27000</td>\n",
       "      <td>5.596100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27050</td>\n",
       "      <td>5.633100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27100</td>\n",
       "      <td>5.594200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27150</td>\n",
       "      <td>5.631500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27200</td>\n",
       "      <td>5.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27250</td>\n",
       "      <td>5.603800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27300</td>\n",
       "      <td>5.588500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27350</td>\n",
       "      <td>5.587100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27400</td>\n",
       "      <td>5.598500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27450</td>\n",
       "      <td>5.587100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27500</td>\n",
       "      <td>5.599200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27550</td>\n",
       "      <td>5.607000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27600</td>\n",
       "      <td>5.621300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27650</td>\n",
       "      <td>5.589800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27700</td>\n",
       "      <td>5.587700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27750</td>\n",
       "      <td>5.609200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27800</td>\n",
       "      <td>5.615500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27850</td>\n",
       "      <td>5.583700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27900</td>\n",
       "      <td>5.626100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27950</td>\n",
       "      <td>5.605700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28000</td>\n",
       "      <td>5.594300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28050</td>\n",
       "      <td>5.592400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28100</td>\n",
       "      <td>5.603100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28150</td>\n",
       "      <td>5.624300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28200</td>\n",
       "      <td>5.625300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28250</td>\n",
       "      <td>5.593200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28300</td>\n",
       "      <td>5.611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28350</td>\n",
       "      <td>5.594200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28400</td>\n",
       "      <td>5.586400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28450</td>\n",
       "      <td>5.605500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28500</td>\n",
       "      <td>5.624200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28550</td>\n",
       "      <td>5.650300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28600</td>\n",
       "      <td>5.550300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28650</td>\n",
       "      <td>5.613100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28700</td>\n",
       "      <td>5.577000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28750</td>\n",
       "      <td>5.581700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28800</td>\n",
       "      <td>5.591400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28850</td>\n",
       "      <td>5.594400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28900</td>\n",
       "      <td>5.567400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28950</td>\n",
       "      <td>5.554800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29000</td>\n",
       "      <td>5.575400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29050</td>\n",
       "      <td>5.602600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29100</td>\n",
       "      <td>5.623300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29150</td>\n",
       "      <td>5.593100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29200</td>\n",
       "      <td>5.592600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29250</td>\n",
       "      <td>5.592400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29300</td>\n",
       "      <td>5.586600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29350</td>\n",
       "      <td>5.559900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29400</td>\n",
       "      <td>5.626300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29450</td>\n",
       "      <td>5.641200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29500</td>\n",
       "      <td>5.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29550</td>\n",
       "      <td>5.581500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29600</td>\n",
       "      <td>5.592300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29650</td>\n",
       "      <td>5.609900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29700</td>\n",
       "      <td>5.550200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29750</td>\n",
       "      <td>5.614500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29800</td>\n",
       "      <td>5.585300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29850</td>\n",
       "      <td>5.599900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29900</td>\n",
       "      <td>5.606400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29950</td>\n",
       "      <td>5.592800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30000</td>\n",
       "      <td>5.594600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30050</td>\n",
       "      <td>5.606200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30100</td>\n",
       "      <td>5.614400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30150</td>\n",
       "      <td>5.539100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30200</td>\n",
       "      <td>5.581000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30250</td>\n",
       "      <td>5.591100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30300</td>\n",
       "      <td>5.585300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30350</td>\n",
       "      <td>5.621300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30400</td>\n",
       "      <td>5.600200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30450</td>\n",
       "      <td>5.609300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30500</td>\n",
       "      <td>5.616300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30550</td>\n",
       "      <td>5.598700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30600</td>\n",
       "      <td>5.601900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30650</td>\n",
       "      <td>5.588100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30700</td>\n",
       "      <td>5.568000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30750</td>\n",
       "      <td>5.614800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30800</td>\n",
       "      <td>5.580800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30850</td>\n",
       "      <td>5.579900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30900</td>\n",
       "      <td>5.612300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30950</td>\n",
       "      <td>5.589000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31000</td>\n",
       "      <td>5.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31050</td>\n",
       "      <td>5.570500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31100</td>\n",
       "      <td>5.593400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31150</td>\n",
       "      <td>5.603400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31200</td>\n",
       "      <td>5.601600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31250</td>\n",
       "      <td>5.592300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31300</td>\n",
       "      <td>5.572600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31350</td>\n",
       "      <td>5.617100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31400</td>\n",
       "      <td>5.613900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31450</td>\n",
       "      <td>5.612700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31500</td>\n",
       "      <td>5.628700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31550</td>\n",
       "      <td>5.582500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31600</td>\n",
       "      <td>5.579200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31650</td>\n",
       "      <td>5.583900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31700</td>\n",
       "      <td>5.600900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31750</td>\n",
       "      <td>5.611400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31800</td>\n",
       "      <td>5.641300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31850</td>\n",
       "      <td>5.619000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31900</td>\n",
       "      <td>5.602200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31950</td>\n",
       "      <td>5.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32000</td>\n",
       "      <td>5.590300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32050</td>\n",
       "      <td>5.597400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32100</td>\n",
       "      <td>5.546600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32150</td>\n",
       "      <td>5.597200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32200</td>\n",
       "      <td>5.575200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32250</td>\n",
       "      <td>5.599300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32300</td>\n",
       "      <td>5.641500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32350</td>\n",
       "      <td>5.606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32400</td>\n",
       "      <td>5.614800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32450</td>\n",
       "      <td>5.600600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32500</td>\n",
       "      <td>5.603000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32550</td>\n",
       "      <td>5.619200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32600</td>\n",
       "      <td>5.617500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32650</td>\n",
       "      <td>5.572900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32700</td>\n",
       "      <td>5.597000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32750</td>\n",
       "      <td>5.592200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32800</td>\n",
       "      <td>5.560200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32850</td>\n",
       "      <td>5.574700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32900</td>\n",
       "      <td>5.622100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32950</td>\n",
       "      <td>5.616700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33000</td>\n",
       "      <td>5.592800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33050</td>\n",
       "      <td>5.623200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33100</td>\n",
       "      <td>5.582400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33150</td>\n",
       "      <td>5.609800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33200</td>\n",
       "      <td>5.611300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33250</td>\n",
       "      <td>5.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33300</td>\n",
       "      <td>5.587000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33350</td>\n",
       "      <td>5.579900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33400</td>\n",
       "      <td>5.609200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33450</td>\n",
       "      <td>5.586000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33500</td>\n",
       "      <td>5.649800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33550</td>\n",
       "      <td>5.600100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33600</td>\n",
       "      <td>5.583100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33650</td>\n",
       "      <td>5.598100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33700</td>\n",
       "      <td>5.592800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33750</td>\n",
       "      <td>5.603400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33800</td>\n",
       "      <td>5.592600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33850</td>\n",
       "      <td>5.580600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33900</td>\n",
       "      <td>5.604900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33950</td>\n",
       "      <td>5.604700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34000</td>\n",
       "      <td>5.547400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34050</td>\n",
       "      <td>5.576100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34100</td>\n",
       "      <td>5.581700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34150</td>\n",
       "      <td>5.616800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34200</td>\n",
       "      <td>5.580900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34250</td>\n",
       "      <td>5.615600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34300</td>\n",
       "      <td>5.626600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34350</td>\n",
       "      <td>5.561500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34400</td>\n",
       "      <td>5.608700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34450</td>\n",
       "      <td>5.550800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34500</td>\n",
       "      <td>5.567500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34550</td>\n",
       "      <td>5.608600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34600</td>\n",
       "      <td>5.560400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34650</td>\n",
       "      <td>5.607100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34700</td>\n",
       "      <td>5.612200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34750</td>\n",
       "      <td>5.605400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34800</td>\n",
       "      <td>5.592100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34850</td>\n",
       "      <td>5.594000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34900</td>\n",
       "      <td>5.564900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34950</td>\n",
       "      <td>5.605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35000</td>\n",
       "      <td>5.586600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35050</td>\n",
       "      <td>5.602000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35100</td>\n",
       "      <td>5.575500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35150</td>\n",
       "      <td>5.563800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35200</td>\n",
       "      <td>5.622600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35250</td>\n",
       "      <td>5.571900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35300</td>\n",
       "      <td>5.577900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35350</td>\n",
       "      <td>5.606900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35400</td>\n",
       "      <td>5.616700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35450</td>\n",
       "      <td>5.595700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35500</td>\n",
       "      <td>5.614200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35550</td>\n",
       "      <td>5.552700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35600</td>\n",
       "      <td>5.590200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35650</td>\n",
       "      <td>5.584200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35700</td>\n",
       "      <td>5.546900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35750</td>\n",
       "      <td>5.580200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35800</td>\n",
       "      <td>5.598100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35850</td>\n",
       "      <td>5.575200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35900</td>\n",
       "      <td>5.610700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35950</td>\n",
       "      <td>5.549700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36000</td>\n",
       "      <td>5.577900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36050</td>\n",
       "      <td>5.589300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36100</td>\n",
       "      <td>5.597300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36150</td>\n",
       "      <td>5.584400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36200</td>\n",
       "      <td>5.620100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36250</td>\n",
       "      <td>5.576300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36300</td>\n",
       "      <td>5.590900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36350</td>\n",
       "      <td>5.595500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36400</td>\n",
       "      <td>5.585700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36450</td>\n",
       "      <td>5.577500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36500</td>\n",
       "      <td>5.588600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36550</td>\n",
       "      <td>5.570900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36600</td>\n",
       "      <td>5.564900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36650</td>\n",
       "      <td>5.600400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36700</td>\n",
       "      <td>5.608500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36750</td>\n",
       "      <td>5.604600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36800</td>\n",
       "      <td>5.593300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36850</td>\n",
       "      <td>5.572900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36900</td>\n",
       "      <td>5.592900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36950</td>\n",
       "      <td>5.585600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37000</td>\n",
       "      <td>5.583300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37050</td>\n",
       "      <td>5.551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37100</td>\n",
       "      <td>5.610700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37150</td>\n",
       "      <td>5.604100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37200</td>\n",
       "      <td>5.585500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37250</td>\n",
       "      <td>5.595500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37300</td>\n",
       "      <td>5.598300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37350</td>\n",
       "      <td>5.589400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37400</td>\n",
       "      <td>5.578800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37450</td>\n",
       "      <td>5.561200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37500</td>\n",
       "      <td>5.555400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37550</td>\n",
       "      <td>5.614100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37600</td>\n",
       "      <td>5.576200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37650</td>\n",
       "      <td>5.568300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37700</td>\n",
       "      <td>5.554400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37750</td>\n",
       "      <td>5.575200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37800</td>\n",
       "      <td>5.616900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37850</td>\n",
       "      <td>5.583300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37900</td>\n",
       "      <td>5.563600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37950</td>\n",
       "      <td>5.582300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38000</td>\n",
       "      <td>5.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38050</td>\n",
       "      <td>5.588400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38100</td>\n",
       "      <td>5.543400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38150</td>\n",
       "      <td>5.572800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38200</td>\n",
       "      <td>5.605200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38250</td>\n",
       "      <td>5.581300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38300</td>\n",
       "      <td>5.569900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38350</td>\n",
       "      <td>5.577900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38400</td>\n",
       "      <td>5.567700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38450</td>\n",
       "      <td>5.597200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38500</td>\n",
       "      <td>5.580800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38550</td>\n",
       "      <td>5.558700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38600</td>\n",
       "      <td>5.613200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38650</td>\n",
       "      <td>5.568700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38700</td>\n",
       "      <td>5.607600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38750</td>\n",
       "      <td>5.610800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38800</td>\n",
       "      <td>5.601100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38850</td>\n",
       "      <td>5.628100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38900</td>\n",
       "      <td>5.613700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38950</td>\n",
       "      <td>5.601500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39000</td>\n",
       "      <td>5.600800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39050</td>\n",
       "      <td>5.574200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39100</td>\n",
       "      <td>5.596100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39150</td>\n",
       "      <td>5.578800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39200</td>\n",
       "      <td>5.613800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39250</td>\n",
       "      <td>5.624200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39300</td>\n",
       "      <td>5.575800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39350</td>\n",
       "      <td>5.560400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39400</td>\n",
       "      <td>5.607000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39450</td>\n",
       "      <td>5.589500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39500</td>\n",
       "      <td>5.564100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39550</td>\n",
       "      <td>5.593300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39600</td>\n",
       "      <td>5.616500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39650</td>\n",
       "      <td>5.562400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39700</td>\n",
       "      <td>5.556700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39750</td>\n",
       "      <td>5.572600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39800</td>\n",
       "      <td>5.539700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39850</td>\n",
       "      <td>5.604100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39900</td>\n",
       "      <td>5.599100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39950</td>\n",
       "      <td>5.579400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40000</td>\n",
       "      <td>5.599200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40050</td>\n",
       "      <td>5.577100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40100</td>\n",
       "      <td>5.570800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40150</td>\n",
       "      <td>5.602500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40200</td>\n",
       "      <td>5.575100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40250</td>\n",
       "      <td>5.628600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40300</td>\n",
       "      <td>5.568800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40350</td>\n",
       "      <td>5.595600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40400</td>\n",
       "      <td>5.577300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40450</td>\n",
       "      <td>5.571300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40500</td>\n",
       "      <td>5.583300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40550</td>\n",
       "      <td>5.592000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40600</td>\n",
       "      <td>5.638900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40650</td>\n",
       "      <td>5.569500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40700</td>\n",
       "      <td>5.581300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40750</td>\n",
       "      <td>5.564900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40800</td>\n",
       "      <td>5.550800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40850</td>\n",
       "      <td>5.588100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40900</td>\n",
       "      <td>5.622000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40950</td>\n",
       "      <td>5.614000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41000</td>\n",
       "      <td>5.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41050</td>\n",
       "      <td>5.605800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41100</td>\n",
       "      <td>5.606200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41150</td>\n",
       "      <td>5.599500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41200</td>\n",
       "      <td>5.586800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41250</td>\n",
       "      <td>5.537100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41300</td>\n",
       "      <td>5.550200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41350</td>\n",
       "      <td>5.584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41400</td>\n",
       "      <td>5.577000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41450</td>\n",
       "      <td>5.577200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41500</td>\n",
       "      <td>5.585800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41550</td>\n",
       "      <td>5.608600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41600</td>\n",
       "      <td>5.587300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41650</td>\n",
       "      <td>5.597200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41700</td>\n",
       "      <td>5.590900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41750</td>\n",
       "      <td>5.584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41800</td>\n",
       "      <td>5.601800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41850</td>\n",
       "      <td>5.581300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41900</td>\n",
       "      <td>5.597400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41950</td>\n",
       "      <td>5.594400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42000</td>\n",
       "      <td>5.584600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42050</td>\n",
       "      <td>5.615700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42100</td>\n",
       "      <td>5.577500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42150</td>\n",
       "      <td>5.592700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42200</td>\n",
       "      <td>5.599800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42250</td>\n",
       "      <td>5.573300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42300</td>\n",
       "      <td>5.583600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42350</td>\n",
       "      <td>5.592200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42400</td>\n",
       "      <td>5.612100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42450</td>\n",
       "      <td>5.590300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42500</td>\n",
       "      <td>5.546200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42550</td>\n",
       "      <td>5.538300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42600</td>\n",
       "      <td>5.574600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42650</td>\n",
       "      <td>5.602100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42700</td>\n",
       "      <td>5.566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42750</td>\n",
       "      <td>5.570700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42800</td>\n",
       "      <td>5.581900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42850</td>\n",
       "      <td>5.580200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42900</td>\n",
       "      <td>5.583200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42950</td>\n",
       "      <td>5.606600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43000</td>\n",
       "      <td>5.590200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43050</td>\n",
       "      <td>5.560900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43100</td>\n",
       "      <td>5.574200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43150</td>\n",
       "      <td>5.577300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43200</td>\n",
       "      <td>5.565600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43250</td>\n",
       "      <td>5.585700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43300</td>\n",
       "      <td>5.613900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43350</td>\n",
       "      <td>5.586500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43400</td>\n",
       "      <td>5.624500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43450</td>\n",
       "      <td>5.599500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43500</td>\n",
       "      <td>5.561900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43550</td>\n",
       "      <td>5.562400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43600</td>\n",
       "      <td>5.619700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43650</td>\n",
       "      <td>5.577100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43700</td>\n",
       "      <td>5.594900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43750</td>\n",
       "      <td>5.603000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43800</td>\n",
       "      <td>5.583600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43850</td>\n",
       "      <td>5.579300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43900</td>\n",
       "      <td>5.571300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43950</td>\n",
       "      <td>5.574600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44000</td>\n",
       "      <td>5.606700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44050</td>\n",
       "      <td>5.583300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44100</td>\n",
       "      <td>5.579700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44150</td>\n",
       "      <td>5.585400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44200</td>\n",
       "      <td>5.575600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44250</td>\n",
       "      <td>5.574500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44300</td>\n",
       "      <td>5.588500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44350</td>\n",
       "      <td>5.539200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44400</td>\n",
       "      <td>5.578900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44450</td>\n",
       "      <td>5.585200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44500</td>\n",
       "      <td>5.566200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44550</td>\n",
       "      <td>5.614000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44600</td>\n",
       "      <td>5.609200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44650</td>\n",
       "      <td>5.593300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44700</td>\n",
       "      <td>5.635400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44750</td>\n",
       "      <td>5.574800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44800</td>\n",
       "      <td>5.572300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44850</td>\n",
       "      <td>5.558100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44900</td>\n",
       "      <td>5.587700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44950</td>\n",
       "      <td>5.597700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45000</td>\n",
       "      <td>5.568100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45050</td>\n",
       "      <td>5.591700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45100</td>\n",
       "      <td>5.566100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45150</td>\n",
       "      <td>5.627300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45200</td>\n",
       "      <td>5.591800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45250</td>\n",
       "      <td>5.611500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45300</td>\n",
       "      <td>5.575900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45350</td>\n",
       "      <td>5.603800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45400</td>\n",
       "      <td>5.592200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45450</td>\n",
       "      <td>5.586700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45500</td>\n",
       "      <td>5.621900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45550</td>\n",
       "      <td>5.579400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45600</td>\n",
       "      <td>5.573700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45650</td>\n",
       "      <td>5.584600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45700</td>\n",
       "      <td>5.621400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45750</td>\n",
       "      <td>5.549200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45800</td>\n",
       "      <td>5.561300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45850</td>\n",
       "      <td>5.594500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45900</td>\n",
       "      <td>5.600400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45950</td>\n",
       "      <td>5.571300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46000</td>\n",
       "      <td>5.619900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46050</td>\n",
       "      <td>5.590700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46100</td>\n",
       "      <td>5.583400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46150</td>\n",
       "      <td>5.595500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46200</td>\n",
       "      <td>5.569300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46250</td>\n",
       "      <td>5.608300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46300</td>\n",
       "      <td>5.592700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46350</td>\n",
       "      <td>5.610700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46400</td>\n",
       "      <td>5.619700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46450</td>\n",
       "      <td>5.576500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46500</td>\n",
       "      <td>5.586000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46550</td>\n",
       "      <td>5.574900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46600</td>\n",
       "      <td>5.598100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46650</td>\n",
       "      <td>5.594000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46700</td>\n",
       "      <td>5.570900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46750</td>\n",
       "      <td>5.575300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46800</td>\n",
       "      <td>5.583200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46850</td>\n",
       "      <td>5.602400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46900</td>\n",
       "      <td>5.588600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46950</td>\n",
       "      <td>5.547800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47000</td>\n",
       "      <td>5.588300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47050</td>\n",
       "      <td>5.572100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47100</td>\n",
       "      <td>5.573300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47150</td>\n",
       "      <td>5.594800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47200</td>\n",
       "      <td>5.571500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47250</td>\n",
       "      <td>5.588900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47300</td>\n",
       "      <td>5.585800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47350</td>\n",
       "      <td>5.563700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47400</td>\n",
       "      <td>5.578600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47450</td>\n",
       "      <td>5.586200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47500</td>\n",
       "      <td>5.556700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47550</td>\n",
       "      <td>5.586000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47600</td>\n",
       "      <td>5.601300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47650</td>\n",
       "      <td>5.623300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47700</td>\n",
       "      <td>5.607500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47750</td>\n",
       "      <td>5.620500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47800</td>\n",
       "      <td>5.574300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47850</td>\n",
       "      <td>5.585200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47900</td>\n",
       "      <td>5.580100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47950</td>\n",
       "      <td>5.566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48000</td>\n",
       "      <td>5.618600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48050</td>\n",
       "      <td>5.620100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48100</td>\n",
       "      <td>5.573400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48150</td>\n",
       "      <td>5.564500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48200</td>\n",
       "      <td>5.550700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48250</td>\n",
       "      <td>5.581900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48300</td>\n",
       "      <td>5.591900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48350</td>\n",
       "      <td>5.577500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48400</td>\n",
       "      <td>5.587600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48450</td>\n",
       "      <td>5.625400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48500</td>\n",
       "      <td>5.568800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48550</td>\n",
       "      <td>5.597900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48600</td>\n",
       "      <td>5.583800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48650</td>\n",
       "      <td>5.592800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48700</td>\n",
       "      <td>5.579600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48750</td>\n",
       "      <td>5.552800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48800</td>\n",
       "      <td>5.562100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48850</td>\n",
       "      <td>5.560700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48900</td>\n",
       "      <td>5.606700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48950</td>\n",
       "      <td>5.582800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49000</td>\n",
       "      <td>5.597800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49050</td>\n",
       "      <td>5.580200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49100</td>\n",
       "      <td>5.589600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49150</td>\n",
       "      <td>5.540900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49200</td>\n",
       "      <td>5.559500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49250</td>\n",
       "      <td>5.607200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49300</td>\n",
       "      <td>5.519100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49350</td>\n",
       "      <td>5.593600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49400</td>\n",
       "      <td>5.572700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49450</td>\n",
       "      <td>5.522600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49500</td>\n",
       "      <td>5.607300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49550</td>\n",
       "      <td>5.564800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49600</td>\n",
       "      <td>5.607000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49650</td>\n",
       "      <td>5.623000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49700</td>\n",
       "      <td>5.549700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49750</td>\n",
       "      <td>5.562900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49800</td>\n",
       "      <td>5.545800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49850</td>\n",
       "      <td>5.579000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49900</td>\n",
       "      <td>5.582200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49950</td>\n",
       "      <td>5.571500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50000</td>\n",
       "      <td>5.617900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50050</td>\n",
       "      <td>5.580800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50100</td>\n",
       "      <td>5.549100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50150</td>\n",
       "      <td>5.580700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50200</td>\n",
       "      <td>5.570300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50250</td>\n",
       "      <td>5.598300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50300</td>\n",
       "      <td>5.542600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50350</td>\n",
       "      <td>5.587900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50400</td>\n",
       "      <td>5.545600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50450</td>\n",
       "      <td>5.587000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50500</td>\n",
       "      <td>5.600800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50550</td>\n",
       "      <td>5.544200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50600</td>\n",
       "      <td>5.561200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50650</td>\n",
       "      <td>5.573100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50700</td>\n",
       "      <td>5.618900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50750</td>\n",
       "      <td>5.593200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50800</td>\n",
       "      <td>5.603500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50850</td>\n",
       "      <td>5.578300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50900</td>\n",
       "      <td>5.542200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50950</td>\n",
       "      <td>5.570600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51000</td>\n",
       "      <td>5.613900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51050</td>\n",
       "      <td>5.571400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51100</td>\n",
       "      <td>5.584100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51150</td>\n",
       "      <td>5.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51200</td>\n",
       "      <td>5.595500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51250</td>\n",
       "      <td>5.571500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51300</td>\n",
       "      <td>5.555900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51350</td>\n",
       "      <td>5.523600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51400</td>\n",
       "      <td>5.587600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51450</td>\n",
       "      <td>5.557400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51500</td>\n",
       "      <td>5.580300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51550</td>\n",
       "      <td>5.600600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51600</td>\n",
       "      <td>5.585200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51650</td>\n",
       "      <td>5.597900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51700</td>\n",
       "      <td>5.635400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51750</td>\n",
       "      <td>5.609100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51800</td>\n",
       "      <td>5.597700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51850</td>\n",
       "      <td>5.546000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51900</td>\n",
       "      <td>5.529700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51950</td>\n",
       "      <td>5.600100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52000</td>\n",
       "      <td>5.599000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52050</td>\n",
       "      <td>5.560100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52100</td>\n",
       "      <td>5.563400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52150</td>\n",
       "      <td>5.542800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52200</td>\n",
       "      <td>5.624100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52250</td>\n",
       "      <td>5.584200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52300</td>\n",
       "      <td>5.588900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52350</td>\n",
       "      <td>5.591700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52400</td>\n",
       "      <td>5.573500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52450</td>\n",
       "      <td>5.558000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52500</td>\n",
       "      <td>5.609600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52550</td>\n",
       "      <td>5.573500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52600</td>\n",
       "      <td>5.593300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52650</td>\n",
       "      <td>5.593100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52700</td>\n",
       "      <td>5.565200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52750</td>\n",
       "      <td>5.575300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52800</td>\n",
       "      <td>5.582800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52850</td>\n",
       "      <td>5.584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52900</td>\n",
       "      <td>5.611800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52950</td>\n",
       "      <td>5.576100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53000</td>\n",
       "      <td>5.596900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53050</td>\n",
       "      <td>5.584800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53100</td>\n",
       "      <td>5.575200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53150</td>\n",
       "      <td>5.563600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53200</td>\n",
       "      <td>5.594400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53250</td>\n",
       "      <td>5.555500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53300</td>\n",
       "      <td>5.557600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53350</td>\n",
       "      <td>5.604000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53400</td>\n",
       "      <td>5.592300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53450</td>\n",
       "      <td>5.601000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53500</td>\n",
       "      <td>5.574600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53550</td>\n",
       "      <td>5.592600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53600</td>\n",
       "      <td>5.575300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53650</td>\n",
       "      <td>5.619900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53700</td>\n",
       "      <td>5.588800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53750</td>\n",
       "      <td>5.591000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53800</td>\n",
       "      <td>5.610600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53850</td>\n",
       "      <td>5.566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53900</td>\n",
       "      <td>5.560800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53950</td>\n",
       "      <td>5.604800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54000</td>\n",
       "      <td>5.587300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54050</td>\n",
       "      <td>5.597200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54100</td>\n",
       "      <td>5.544300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54150</td>\n",
       "      <td>5.556200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54200</td>\n",
       "      <td>5.571000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54250</td>\n",
       "      <td>5.596200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54300</td>\n",
       "      <td>5.595500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54350</td>\n",
       "      <td>5.549700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54400</td>\n",
       "      <td>5.567100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54450</td>\n",
       "      <td>5.553700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54500</td>\n",
       "      <td>5.567600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54550</td>\n",
       "      <td>5.603000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54600</td>\n",
       "      <td>5.615700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54650</td>\n",
       "      <td>5.579600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54700</td>\n",
       "      <td>5.596600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54750</td>\n",
       "      <td>5.564100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54800</td>\n",
       "      <td>5.583500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54850</td>\n",
       "      <td>5.570300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54900</td>\n",
       "      <td>5.567400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54950</td>\n",
       "      <td>5.562400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55000</td>\n",
       "      <td>5.575500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55050</td>\n",
       "      <td>5.613200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55100</td>\n",
       "      <td>5.593400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55150</td>\n",
       "      <td>5.589500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55200</td>\n",
       "      <td>5.555100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55250</td>\n",
       "      <td>5.558300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55300</td>\n",
       "      <td>5.556500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55350</td>\n",
       "      <td>5.565600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55400</td>\n",
       "      <td>5.606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55450</td>\n",
       "      <td>5.588200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55500</td>\n",
       "      <td>5.582600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55550</td>\n",
       "      <td>5.578000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55600</td>\n",
       "      <td>5.633100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55650</td>\n",
       "      <td>5.597000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55700</td>\n",
       "      <td>5.613900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55750</td>\n",
       "      <td>5.599400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55800</td>\n",
       "      <td>5.611800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55850</td>\n",
       "      <td>5.606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55900</td>\n",
       "      <td>5.560500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55950</td>\n",
       "      <td>5.594200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56000</td>\n",
       "      <td>5.548400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56050</td>\n",
       "      <td>5.572300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56100</td>\n",
       "      <td>5.564900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56150</td>\n",
       "      <td>5.609300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56200</td>\n",
       "      <td>5.586100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56250</td>\n",
       "      <td>5.557900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56300</td>\n",
       "      <td>5.605500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56350</td>\n",
       "      <td>5.600800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56400</td>\n",
       "      <td>5.586800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56450</td>\n",
       "      <td>5.569300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56500</td>\n",
       "      <td>5.585300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56550</td>\n",
       "      <td>5.606000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56600</td>\n",
       "      <td>5.587200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56650</td>\n",
       "      <td>5.545400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56700</td>\n",
       "      <td>5.623100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56750</td>\n",
       "      <td>5.623400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56800</td>\n",
       "      <td>5.607300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56850</td>\n",
       "      <td>5.593700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56900</td>\n",
       "      <td>5.561400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56950</td>\n",
       "      <td>5.550900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57000</td>\n",
       "      <td>5.540400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57050</td>\n",
       "      <td>5.596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57100</td>\n",
       "      <td>5.578400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57150</td>\n",
       "      <td>5.584800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57200</td>\n",
       "      <td>5.576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57250</td>\n",
       "      <td>5.592400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57300</td>\n",
       "      <td>5.598700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=57300, training_loss=5.629599132704277, metrics={'train_runtime': 5505.4324, 'train_samples_per_second': 41.631, 'train_steps_per_second': 10.408, 'total_flos': 3992237037060096.0, 'train_loss': 5.629599132704277, 'epoch': 2.0})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d23bf9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luyingli\\.conda\\envs\\yolo5\\lib\\site-packages\\accelerate\\accelerator.py:451: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2366' max='573000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [  2366/573000 04:02 < 16:14:40, 9.76 it/s, Epoch 0.08/20]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>5.482700</td>\n",
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       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>5.451400</td>\n",
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       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>5.457400</td>\n",
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       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>5.446000</td>\n",
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       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>5.509600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>5.509700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>5.476700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>5.456700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>5.468900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>5.436600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>5.495300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>5.498100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>5.465700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>5.481400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>5.477400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>5.514800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>850</td>\n",
       "      <td>5.481400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>5.437700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>950</td>\n",
       "      <td>5.456600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>5.479500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1050</td>\n",
       "      <td>5.516800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>5.479500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1150</td>\n",
       "      <td>5.469300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>5.435100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1250</td>\n",
       "      <td>5.438100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>5.471400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1350</td>\n",
       "      <td>5.472600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1400</td>\n",
       "      <td>5.467700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1450</td>\n",
       "      <td>5.420900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>5.447000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1550</td>\n",
       "      <td>5.493400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1600</td>\n",
       "      <td>5.502500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1650</td>\n",
       "      <td>5.454400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1700</td>\n",
       "      <td>5.489800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1750</td>\n",
       "      <td>5.519700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1800</td>\n",
       "      <td>5.502100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1850</td>\n",
       "      <td>5.540500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1900</td>\n",
       "      <td>5.458300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1950</td>\n",
       "      <td>5.470700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>5.458200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2050</td>\n",
       "      <td>5.504000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2100</td>\n",
       "      <td>5.482700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2150</td>\n",
       "      <td>5.454300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2200</td>\n",
       "      <td>5.489900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2250</td>\n",
       "      <td>5.454000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2300</td>\n",
       "      <td>5.470200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2350</td>\n",
       "      <td>5.444700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[22], line 33\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Trainer\n\u001b[0;32m     24\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[0;32m     25\u001b[0m     model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[0;32m     26\u001b[0m     args\u001b[38;5;241m=\u001b[39mtraining_args,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     30\u001b[0m     data_collator\u001b[38;5;241m=\u001b[39mdata_collator,\n\u001b[0;32m     31\u001b[0m )\n\u001b[1;32m---> 33\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\yolo5\\lib\\site-packages\\transformers\\trainer.py:1780\u001b[0m, in \u001b[0;36mTrainer.train\u001b[1;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[0;32m   1778\u001b[0m         hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[0;32m   1779\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1780\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1781\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1782\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1783\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1784\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1785\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\yolo5\\lib\\site-packages\\transformers\\trainer.py:2118\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[1;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[0;32m   2115\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_begin(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[0;32m   2117\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39maccumulate(model):\n\u001b[1;32m-> 2118\u001b[0m     tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   2121\u001b[0m     args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[0;32m   2122\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[0;32m   2123\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[0;32m   2124\u001b[0m ):\n\u001b[0;32m   2125\u001b[0m     \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[0;32m   2126\u001b[0m     tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
      "File \u001b[1;32m~\\.conda\\envs\\yolo5\\lib\\site-packages\\transformers\\trainer.py:3045\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[1;34m(self, model, inputs)\u001b[0m\n\u001b[0;32m   3043\u001b[0m         scaled_loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m   3044\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 3045\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3047\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mdetach() \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n",
      "File \u001b[1;32m~\\.conda\\envs\\yolo5\\lib\\site-packages\\accelerate\\accelerator.py:2159\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[1;34m(self, loss, **kwargs)\u001b[0m\n\u001b[0;32m   2157\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlomo_backward(loss, learning_rate)\n\u001b[0;32m   2158\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2159\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\.conda\\envs\\yolo5\\lib\\site-packages\\torch\\_tensor.py:492\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    482\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    484\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    485\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    490\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[0;32m    491\u001b[0m     )\n\u001b[1;32m--> 492\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    493\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\yolo5\\lib\\site-packages\\torch\\autograd\\__init__.py:251\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    246\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    248\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    250\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 251\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='saves',                         # 输出路径，包括模型检查点、中间文件等    \n",
    "    overwrite_output_dir=True,                  # 是否覆写 \n",
    "    do_train=True,                              # 是否做训练    \n",
    "    do_eval=True,                               # 是否做评估    \n",
    "    eval_steps=1000,                            # 评估步骤间隔    \n",
    "    per_device_train_batch_size=4,              # 每设备批次    \n",
    "    gradient_accumulation_steps=1,              # 梯度累计步大小，省显存，但小模型没必要，用 1 收敛比较快    \n",
    "    learning_rate=1e-5,                         # 学习率大小    \n",
    "    lr_scheduler_type='cosine',                 # 学习率调度策略，LLM 训练一般都用余弦    \n",
    "    bf16=torch.cuda.is_bf16_supported(),        # 尝试配置 bf16    \n",
    "    fp16=not torch.cuda.is_bf16_supported(),    # bf16 不行就上 fp16    \n",
    "    logging_steps=50,                           # 打印步骤间隔    report_to=None,                             # 日志输出目标，不想用 wandb 可以设置为 None    \n",
    "    num_train_epochs=20,                         # 训练轮数，2 ~ 3 即可    \n",
    "    save_steps=1000,                            # 检查点保存步骤间隔    \n",
    "    save_total_limit=2,                         # output_dir 内留存的检查点最大数目    seed=3407                                   # 随机种子\n",
    ")\n",
    "\n",
    "\n",
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_train_dataset,\n",
    "    eval_dataset=tokenized_val_dataset,\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    ")\n",
    "\n",
    "trainer.train()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68b74837",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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